Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
arXiv:2604.24806v2 Announce Type: replace-cross Abstract: Modern Deep Learning Recommendation Models (DLRMs) follow scaling laws with sequence length, driving the frontier toward ultra-long User Interaction History (UIH). However, the industry-standard "Fat Row" paradigm, which pre-materializes these sequences into every training example, creates a storage and I/O wall where data infrastructure usage exceeds GPU training capacity due to data redundancy that is amplified in multi-tenant environments where models with vastly different sequence length requirements share a union dataset. We presen
The continuous drive for more complex and personalized AI recommendation systems, coupled with growing data scales, is exposing fundamental infrastructure bottlenecks.
This development addresses a critical scaling limitation for AI models, especially in data-intensive applications like recommendation systems, directly impacting efficiency and cost of training.
New approaches to data materialization could unlock higher performance and larger sequence lengths for DLRMs, potentially altering infrastructure requirements for large-scale AI training.
- · Companies with large recommendation systems
- · Cloud providers offering optimized AI infrastructure
- · Researchers in efficient data management for AI
- · Companies relying on traditional 'Fat Row' data paradigms
- · Inefficient data infrastructure designs
Reduced I/O and storage costs for training ultra-long sequence recommendation models.
Improved accuracy and personalization in AI recommendations due to access to more extensive user history.
Accelerated development of more complex and resource-intensive AI models across other domains due to shared infrastructure learnings.
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Read at arXiv cs.AI